export_func.export(model, sess, signature_name=mission, version=fold + 1)

def export(model, sess, signature_name, export_path=root_path + '/all_in_one/demo/exported_models/', version=1):

    # export path

    export_path = os.path.join(os.path.realpath(export_path), signature_name, str(version))

    print('Exporting trained model to {} ...'.format(export_path))

    builder = tf.saved_model.builder.SavedModelBuilder(export_path)

    # Build the signature_def_map.

    classification_w = tf.saved_model.utils.build_tensor_info(model.w)

    # classification_is_training = tf.saved_model.utils.build_tensor_info(model.is_training)

    classification_dropout_keep_prob_mlp = tf.saved_model.utils.build_tensor_info(

        model.dropout_keep_prob_mlp)

    # score

    classification_outputs_scores = tf.saved_model.utils.build_tensor_info(model.y)

    classification_signature = tf.saved_model.signature_def_utils.build_signature_def(

        inputs={tf.saved_model.signature_constants.CLASSIFY_INPUTS: classification_w},

        outputs={

            tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:

            classification_outputs_scores

        },

        method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME)  # 'tensorflow/serving/classify'

    prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(

        inputs={'input_plh': classification_w, 'dropout_keep_prob_mlp':

                classification_dropout_keep_prob_mlp,

                # 'is_training': classification_is_training

                },

        outputs={'scores': classification_outputs_scores},

        method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)  # 'tensorflow/serving/predict'

    builder.add_meta_graph_and_variables(

        sess, [tf.saved_model.tag_constants.SERVING],

        signature_def_map={

            signature_name: prediction_signature,

            tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: classification_signature,

        })

    builder.save()

def classify(self,  sents):

        self.sents=self.sents2id(sents)

        hostport = '192.168.31.186:6000'

        # grpc

        host, port = hostport.split(':')

        channel = implementations.insecure_channel(host, int(port))

        stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)

        # build request

        request = predict_pb2.PredictRequest()

        request.model_spec.name = self.model_name

        request.model_spec.signature_name = self.signature_name

        request.inputs['input_plh'].CopyFrom(

            tf.contrib.util.make_tensor_proto(self.sents, dtype=tf.int32))

        request.inputs['dropout_keep_prob_mlp'].CopyFrom(

            tf.contrib.util.make_tensor_proto(1.0, dtype=tf.float32))

        model_result = stub.Predict(request, 60.0)

        model_result = np.array(model_result.outputs['scores'].float_val)

        model_result = [model_result.tolist()][0]

        index, _ =max(enumerate(model_result), key=operator.itemgetter(1))

        if index>0:

            label = self.label_dict[index-1]

        else:

            label = ""

        # print("index:{}\tlabel:{}".format(index, label))

        if self.encode == "part" :

            if label:

                label=self.part[label]

            else:

                label = "凌晨"

        if self.encode == "type" :

            if label:

                label=self.type[label]

            else:

                label = "录像"

        if self.encode == "door" and label:

            label = self.gate[label]

        return label